Papers
DONIS: Importance Sampling for Training Physics-Informed DeepONet
Shudong Huang, Rui Huang, Ming Hu et al.
Do not Abstain! Identify and Solve the Uncertainty
Jingyu Liu, JingquanPeng JingquanPeng, Xiaopeng Wu et al.
Do Not DeepFake Me: Privacy-Preserving Neural 3D Head Reconstruction Without Sensitive Images
Jiayi Kong, Xurui Song, Shuo Huai et al.
Do Not Design, Learn: A Trainable Scoring Function for Uncertainty Estimation in Generative LLMs
Duygu Nur Yaldiz, Yavuz Faruk Bakman, Baturalp Buyukates et al.
Don’t Erase, Inform! Detecting and Contextualizing Harmful Language in Cultural Heritage Collections
Orfeas Menis Mastromichalakis, Jason Liartis, Kristina Rose et al.
Don't flatten, tokenize! Unlocking the key to SoftMoE's efficacy in deep RL
Ghada Sokar, Johan Samir Obando Ceron, Aaron Courville et al.
Don’t Forget the Base Retriever! A Low-Resource Graph-based Retriever for Multi-hop Question Answering
André Melo, Enting Chen, Pavlos Vougiouklis et al.
Don’t Get Lost in the Trees: Streamlining LLM Reasoning by Overcoming Tree Search Exploration Pitfalls
Ante Wang, Linfeng Song, Ye Tian et al.
Don’t Half-listen: Capturing Key-part Information in Continual Instruction Tuning
Yongquan He, Wenyuan Zhang, Xuancheng Huang et al.
Don’t Miss the Forest for the Trees: Attentional Vision Calibration for Large Vision Language Models
Sangmin Woo, Donguk Kim, Jaehyuk Jang et al.
Don’t Reinvent the Wheel: Efficient Instruction-Following Text Embedding based on Guided Space Transformation
Yingchaojie Feng, Yiqun Sun, Yandong Sun et al.
Don’t Say No: Jailbreaking LLM by Suppressing Refusal
Yukai Zhou, Jian Lou, Zhijie Huang et al.
Don’t Score too Early! Evaluating Argument Mining Models on Incomplete Essays
Nils-Jonathan Schaller, Yuning Ding, Thorben Jansen et al.
Don't Shake the Wheel: Momentum-Aware Planning in End-to-End Autonomous Driving
Ziying Song, Caiyan Jia, Lin Liu et al.
DON’T STOP ME NOW: EMBEDDING BASED SCHEDULING FOR LLMS
Rana Shahout, eran malach, Chunwei Liu et al.
Don’t stop pretraining! Efficiently building specialised language models in resource-constrained settings.
Sven Najem-Meyer, Frédéric Kaplan, Matteo Romanello
Don’t Sweat the Small Stuff: Segment-Level Meta-Evaluation Based on Pairwise Difference Correlation
Colten DiIanni, Daniel Deutsch
Don’t Take it Literally! Idiom-aware Vietnamese Translation via In-context Learning
Luan Thanh Nguyen, Parisa Kordjamshidi
Don’t Take it Literally! Idiom-aware Vietnamese Translation via In-context Learning
Luan Thanh Nguyen, Parisa Kordjamshidi
Don’t Take the Premise for Granted: Evaluating the Premise Critique Ability of Large Language Models
Jinzhe Li, Gengxu Li, Yi Chang et al.
Don't Take Things Out of Context: Attention Intervention for Enhancing Chain-of-Thought Reasoning in Large Language Models
Shaotian Yan, Chen Shen, Wenxiao Wang et al.
Don’t Think It Twice: Exploit Shift Invariance for Efficient Online Streaming Inference of CNNs
Christodoulos Kechris, Jonathan Dan, Jose Miranda et al.
Don’t Touch My Diacritics
Kyle Gorman, Yuval Pinter
DONUT: A Decoder-Only Model for Trajectory Prediction
Markus Knoche, Daan de Geus, Bastian Leibe
Doodle Your Keypoints: Sketch-Based Few-Shot Keypoint Detection
Subhajit Maity, Ayan Kumar Bhunia, Subhadeep Koley et al.